Systems and methods for computing the presence of self-luminous elements in an image

ABSTRACT

Embodiments of the present invention comprise systems and methods for estimating self-luminous image elements and modifying images and image corrections for the presence of self-luminous elements.

TECHNICAL FIELD

The present invention relates generally to digital image processing andmore particularly to the detection of aperture colors or self-luminousobjects in an image.

BACKGROUND

The reflected light reaching an image sensor is dependent on the lightthat illuminates the subject matter of the image. Different illuminantswill produce different sensor values from the surfaces of the imagesubject matter. The human visual system approximately compensates theseshifts in reflected light to a perceived norm such that surface colorsappear color constant. However, when images are captured on media andviewed under a light source different than the source in the imagescene, these natural corrections do not take place. Accordingly, it isoften desirable for recorded images to be color-balanced to a referencelight source in order to appear as they would to the natural eye. Thisbalancing or color correction can be performed once the scene illuminantis identified.

There are many known methods for identification of a light source orilluminant in an image scene. However, the conventional correctionalgorithms assume that all image pixels represent reflecting surfaces.When an image contains self-luminous objects such as sky and other lightsources, the surface-pixel assumption is violated. When an imagecontains a significant portion of non-reflective, self-luminous objects,conventional methods will fail and the image illuminant will beincorrectly determined. For example, if an image contains blue sky andthe color-balance algorithm assumes that all pixels are reflectiveobjects, “bluish” pixels could be taken as evidence that theillumination of the scene is bluish. Because a color correction isapproximately the opposite hue of the estimated illuminant, thecorrection for a bluish illuminant would be to shift the image in ayellowish direction. This correction might produce an overly yellowishground/surface region and a desaturated sky region.

These color correction, color balance or color constancy algorithmsgenerally do not address the question of how to handle images containingluminous objects, which are also referred to herein as self-luminousobjects. They have, rather, focused on images where the surface-pixelassumption is satisfied (e.g., uniformly illuminated Mondrian-likeimages).

It would be advantageous if a method existed that permitted accuratedigital image illuminant corrections to be made for images containingsignificant exception regions, such as self-luminous regions.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments will become more fully apparent from thefollowing description and appended claims, taken in conjunction with theaccompanying drawings. Understanding that these drawings depict onlytypical embodiments and are, therefore, not to be considered limiting ofthe invention's scope, the embodiments will be described with additionalspecificity and detail through use of the accompanying drawings inwhich:

FIG. 1 a is a flowchart illustrating methods of self-luminous elementdetection of some embodiments of the present invention;

FIG. 1 b is a flowchart illustrating methods of self-luminous elementdetection and element weighting of some embodiments of the presentinvention;

FIG. 2 is a an exemplary image used to illustrate elements ofembodiments of the present invention;

FIG. 3 a is an image illustrating a likelihood of being self-luminousbased on pixel position relative to an image boundary;

FIG. 3 b is an image illustrating a likelihood of being self-luminousbased on color characteristics;

FIG. 3 c is an image illustrating a likelihood of being self-luminousbased on luminance characteristics;

FIG. 3 d is an image illustrating a combined likelihood of beingself-luminous based on the combination of factors illustrated in FIGS. 3a, 3 b and 3 c;

FIG. 4 is a diagram illustrating an exemplary self-luminous probabilitydistribution

FIG. 5 is a flowchart illustrating some steps of embodiments of thepresent invention;

FIG. 6 is a diagram illustrating the various color distributionsreflected from selected illuminants;

FIG. 7 is a diagram illustrating the chromaticities of the illuminantsshown in FIG. 6;

FIG. 8 is a flowchart illustrating methods of embodiments employing aweighting factor related to a likelihood of being self-luminous;

FIG. 9 is a flowchart illustrating methods of embodiments employing acorrection factor; and

FIG. 10 is a flowchart illustrating methods of embodiments whereinself-luminous elements are distinguished during correction.

DETAILED DESCRIPTION

In some embodiments of the present invention, methods for determiningimage self-luminous regions are employed. Digital image correctionparameters may be calculated in response to determining theself-luminous regions. Image self-luminous regions can be determinedfrom image feature measures. For example, image feature measures can bepixel color characteristics, pixel luminance and/or pixel positionparameters. In some embodiments, self-luminous regions may be determinedin response to bright pixel luminance, where bright pixel luminance isdefined as relatively bright with respect to surrounding image regionsand/or brighter than a threshold brightness.

In some embodiments, self-luminous regions may be determined using ageneral function of color characteristics with a plurality of terms suchas a function of chromaticity, hue angle, color saturation, orbrightness.

In other embodiments, self-luminous regions may be determined inrelation to the position of pixels with respect to the image edges orother geometric image characteristics.

In many algorithms of embodiments of the present invention, thecorrection takes the form of a matrix that is applied in a linearcolorspace such as the tristimulus values of CIE 1931 XYZ. Inputtristimulus values can be multiplied by a matrix to produce a set ofcolor-corrected values as follows:x′=M·xwhere

-   -   x is a vector of tristimulus values    -   M is a color correction matrix    -   x′ is the vector of corrected values.        The color correction can be performed in other color spaces,        including linear transformations of CIE 1931 XYZ or RGB sensor        spaces.

One view of color correction is that the critical step is estimating anilluminant for the scene and then from this estimate a correction matrixcan be defined. This approach seeks to estimate surface reflectancefunctions and the illumination at each pixel. The original sensor valuescan then be mapped to values under a canonical illuminant. This processassumes that the surface-pixel assumption is satisfied.

One method of defining a correction matrix is in terms of linear modelsfor reflectances and illuminants and an estimated illuminant for thescene.. After performing a principle components analysis (or singularvalue decomposition, SVD) on a large set of reflectance or illuminantfunctions (e.g., those of the matte Munsell chipset or measured daylightpower spectra), typical illuminants and surfaces can be approximatedusing low-dimensional linear models. Functions can then be approximatedwithin the linear model by defining sets of weights:r=B _(s) w _(s)e=B _(e) w _(e)where

-   -   r is a 1-by-nwavelengths vector of approximated reflectance        values;    -   e is a 1-by-nwavelengths vector of approximated illuminant        spectral power values;    -   w_(s) is a set of weights within the surface linear model;    -   w_(e) is a set of weights within the illuminant linear model;    -   B_(s) is a set of linear basis functions for surface        reflectance; and,    -   B_(e) is a set of linear basis functions for illuminant spectral        power.

Colorspace coordinates, x, can be computed for a reflectance functionrendered under an illuminant.x=TEB _(s) w _(s),where

-   -   E is a diagonal [nWavelengths, nWavelengths] matrix representing        the illuminant, i.e., E=diag(B_(e)w_(e)).

T is a matrix representing the conversion from wavelength domain tocolor coordinates (e.g., CIE 1931 color matching functions or a set ofsensor response functions).

This equation for computing sensor values describes the physics ofreflecting an illuminant spectral power distribution from a mattereflecting surface and applying the signal to a set of sensors. Thedimensions of the bases are [nWavelengths, nBs] and [nwavelengths, nBe].

Given an illuminant estimate for a first scene, it is possible to invertthe mapping from color coordinates to surface weights. The surfaceweights may then be re-rendered under an illuminant in a second scene(e.g., a standard illumination condition such as CIE D6500). Thecolor-corrected values under the second illuminant, x′, can be computedas:x′=M·xwhere

-   -   M=TE₂B_(x) (TE₁B_(s))⁻¹    -   E₂=diag(e_(canonical))    -   E₁=diag(B_(E)w_(E))    -   e_(canonical) is the spectral power distribution of a canonical        illuminant.

Some alternative methods of defining a correction matrix imposeconstraints on the form of the correction. For example, in some methods,the correction matrix must be diagonal rather than fully-populated. Inother methods the gain values are defined so as to map a neutral objectunder the estimated illuminant to neutral values under the referenceilluminant. For each form of correction matrix, it is necessary to mapfrom estimated illumination parameters to correction parameters.

Self-luminous or non-reflecting image regions can lead to problemsincluding incorrectly biasing the illuminant estimate or problems incolor correction due to violations of the assumptions underlying thecolor correction transform.

In some embodiments of the present invention, image elements, such aspixels or groups thereof, are evaluated to determine whether they arelikely to represent self-luminous objects in the image. These elementsmay be more likely to be self-luminous when they are located inproximity to the edges of the image. For example, and not by way oflimitation, in images that contain regions of sky, a self-luminous area,the self-luminous sky area is typically located along the top edge ofthe image. When images are rotated, such as from landscape to portraitformat, top edges may become side edges and vice versa. Accordingly, insome embodiments of the present invention, proximity to any edge can bean indicator of self-luminance. In other embodiments, image orientationalgorithms may be used to identify the top, bottom and sides of animage. In other embodiments, an image may comprise metadata or someother indication of image orientation. In these embodiments withidentified orientation, proximity to particular edges may be indicativeof self-luminance. For example and not by way of limitation, a pixel'sproximity to the top of the image may be more indicative ofself-luminance than proximity to the side or bottom of the image.

In some embodiments, an element's likelihood of being self-luminous maybe determined in relation to the luminance or brightness of the element.In some embodiments, the output of this function can be viewed as acontinuous-tone grayscale image, where lighter regions correspond toregions likely to be self-luminous objects and darker regions correspondto reflecting surfaces.

In some embodiments, an element's chromaticity or color characteristicscan be used to determine self-luminance. When an element's chromaticityis similar to the chromaticity of known illuminants or commonself-luminous objects, that element is more likely to be self-luminous.

Some embodiments of the present invention comprise the steps illustratedin the chart of FIG. 1 a wherein an image element, such as a pixel orregion of interest, is evaluated 10 to determine its characteristicssuch as location, chromaticity or color characteristics and/or luminancevalue. The location of the element is evaluated 12 to determine itsproximity to image edges and, if the element is sufficiently proximateto an edge, a specific edge or some other geometric image boundary theelement is considered more likely to be self-luminous and a flag ordecision variable value is set 11 or incremented according to thislikelihood.

The color characteristics of the element are then evaluated 14 and theelement is considered more likely to be self-luminous if the colorcharacteristics match those of known illuminants or self-luminousobjects. Color characteristics comprise chromaticity, luminance andother attributes. When these characteristics indicate a likelihood ofbeing self-luminous, a flag or decision variable value is set 13according to this likelihood.

These evaluations 12, 14 and the associated flag or decision variablesetting may be performed in any order as needed for specificapplications. The results of these evaluations can be combined 16, suchas by variable addition, Boolean logic or by other methods, and, ifsufficient evidence exists, the element can be considered likely to beself-luminous 18.

In other embodiments of the present invention, as illustrated in FIG.1b, an image element can be evaluated and classified according to aweighting factor or score. In these embodiments, an element is evaluatedto determine whether its location is proximate to image edges, specificedges or some other image boundary characteristic. If the location issufficiently proximate to one of more of these characteristics, theweight factor or score of that element is increased 24 and the elementis further evaluated.

If an element's color characteristics are similar to those of knownself-luminous objects 26, such as blue sky, overcast sky,tungsten-incandescent light, fluorescent light and others, that elementcan be considered likely to be self-luminous and the element's weightingfactor or score can be increased 28 to reflect this likelihood.

If an element's luminance characteristics are found to be similar tothose of known self-luminous objects 30, the weighting factor or scoreof that element can be increased 32 to reflect a higher likelihood thatthe element is self-luminous. When the score or weighting factor of anelement is sufficiently high, the element may be consideredself-luminous.

The methods of some embodiments of the present invention may beillustrated with reference to FIG. 2, which is an original digital imageand FIGS. 3 a-3 d, which is a set of images derived from FIG. 2depicting the likelihood of self-luminosity based on element location,chromaticity or color characteristics, and/or luminance parameters. Inthe images in FIGS. 3 a-3 d, white areas represent areas of highlikelihood, while black areas represent low likelihood.

In some embodiments of the present invention, the self-luminous decisionvariable, s, can be computed from the pixel color values, x, and pixelposition, (i,j), as:s=f(x,i,j)

In some embodiments, independence between the factors can also beassumed. This is convenient computationally at the expense of not takingadvantage of correlations between terms (e.g., that sky is likely to beboth bluish and of high luminance).s=g(x)*h(i,j)

The term dependent on color values can be decomposed into separableterms based on chromaticity, c, and luminance, Y:s=g ₁(c)*g₂(Y)*h(i,j)

For g₁(c), a function can be designed that has a maximum at the hueangle of “blue sky” and that decreases as the hue angle deviates fromthis value. We base g₂(Y) on luminance distributions for a large numberof sky regions. For h(i,j), the simple assumption can be made that skyis more likely near the edges of the image. Here h(i,j) is a linearfunction of pixel row number. Note that these choices for g₁(C) andg₂(Y) strongly bias the model toward identifying regions of “blue sky”or another illuminant or group of illuminants as self-luminous objects.

FIG. 2 is an image containing surface regions 42 and sky regions 40.FIG. 3 illustrates a decision variable map for each self-luminositylikelihood function. We use “xy” to denote chromaticity and “Y” todenote luminance, after the CEE 1931 xyY colorspace. In the groundregions, the final decision variable is near zero as illustrated by theblack color in all four images in FIGS. 3 a-3 d.

In FIG. 3 a, the likelihood of an element being self-luminous near thetop edge of the image is shown by the lighter shading that getsprogressively darker as the location becomes more distant from the topedge.

In FIG. 3 b, the elements or pixels that have chrominance similar to“blue sky” or another illuminant or group of illuminants are shown inwhite, while those “distant” from that range of hues are shown in darkershades.

In FIG. 3 c, the elements or pixels that have luminance values similarto those of known self-luminous objects are shown in lighter shades. Inthis exemplary image case, few pixels have high luminance as the sky inthis image is relatively dark.

FIG. 3 d shows a composite image representing a combination of imagesshown in FIGS. 3 a-3 c. In this figure, only elements in the sky areshown in lighter shades thereby signifying a successful application ofthe combined analysis.

FIG. 4 is a diagram illustrating the distribution of self-luminositydecision variables corresponding to FIG. 3 d. The “sky” regioncorresponds to the values in the 0.2-0.35 range.

Embodiments of the present invention may be used in conjunction withcolor balance or color constancy estimation methods and color balancecorrection in digital images. In some embodiments of the presentinvention, as shown in FIG. 5, inventive methods and systems may be usedto determine self-luminous elements 110. These methods and systems mayfurther influence the determination of an image illuminant 115 and thecorrection used to achieve color balance 120. These systems and methodsmay also have an effect on the application of the color balancecorrection 130.

Most color-balance algorithms can be modified to use a self-luminousclassification map to derive color-balance parameter estimates from onlythose pixels classified as non-self-luminous. These modified algorithmswould produce estimates with reduced bias due to the presence ofself-luminous image regions.

For example, a greyworld algorithm can be modified to compute a weightedaverage of the scene chromaticities where the weight would be reducedfor pixels likely to be self-luminous.

Consider the illuminant estimation method that selects the illuminantparameters e_(i) such that the function f(e_(i)) is maximized for theimage, I:f(e _(i))=Σp(x _(k) |I, S _(k)=0)*p(x _(k) |e _(i))where

-   -   x_(k) represents a vector of color values    -   S_(k) represents a binary variable indicating the state of        self-luminous (S=1) or surface (S=0)    -   e_(i) represents a set of parameters for the ith illuminant        p(x_(k)|I, S_(k)=0) represents the likelihood of observing color        coordinates x_(k) in an image, I, for the non-self-luminous        pixels. p(x_(k)|e_(i)) represents the likelihood of observing        color coordinates x_(k) under illuminant e_(i). This term        represents a scene/image color probability model that is indexed        by illuminant. When the color frequencies observed in the image        match the color frequencies expected under a particular        illuminant, the value f will be large. The illuminant        corresponding to the maximum of f(e_(i)) is the illuminant most        consistent with the surface pixels in the image. For a discrete        set of color coordinates, the observed color likelihood function        can be approximated as:        p(x _(k) |I, S=0)=n _(k) /N _(S=0)        where    -   n_(k) represents the number of surface pixels within a distance        ε of coordinate x_(k)    -   N_(S=)0 is the number of pixels in the surface class

An alternative method of estimating an illuminant from an image, I, isto directly use the self-luminous decision variable rather than aclassification map. Consider the illuminant estimation method thatselects the illuminant parameters e; such that the function g(e_(i)) ismaximized:g(e _(i))=Σw _(k) *p(x _(k) |e _(i))where

-   -   w_(k) represents a weight for a kth vector of color values that        is dependent on the corresponding self-luminous decision        variable

The value w_(k) may be computed from pixel self-luminous decisionvariable, s:w _(k)=1−swhere s is normalized to have range 0-1.

p(x_(k) |e _(i)) represents the likelihood of observing colorcoordinates x_(k) under illuminant e_(i). The term w_(k) weights thecontributions of pixels that are non-self-luminous more than pixels thatare self-luminous. Thus the value g(e_(i)) will be a measure of howconsistent illuminant e_(i) is with the surface pixels in the image.

For a discrete set of color coordinates, the weight may be computed as:w_(k)=Σw_(j)where

-   -   j indexes over the pixels within a distance P of coordinate        x_(k)

In some embodiments, Bayesian color balance estimation techniquesformulate the problem as selecting the illuminant, e_(i), that maximizesthe illuminant posterior probability distribution, p(e_(i)|I), of theilluminant parameters conditioned on the image data. Bayes' Law relatesthe posterior to the prior, p(e_(i)), and image likelihood function,p(I|e_(i)):p(e _(i) |I)=k·p(I|e _(i))·p(e _(i))where

-   -   k is a normalizing constant    -   p(e_(i)) represents the likelihood of different illuminants    -   p(I|e_(i)) represents the likelihood the image was illuminated        by illuminant e_(i).

If pixels are assumed independent from one another, then the colorlikelihood of the image is the product of the likelihoods of eachpixel's color values, x_(j), given the illuminant:p(I|e _(i))=Πp(x _(j) |e _(i))

One method of introducing a self-luminous classification map into thepixel likelihood function is to let a binary variable S indicate that apixel is self-luminous (S=1) or non-self-luminous (S=0). The colorlikelihood function is equal to,p(x _(j) |e _(i))=p(x _(j) |e _(i) , S=0)p(S=0)+p(x _(j) |e _(i) ,S=1)p(S=1)

For the case where a pixel is self-luminous, the color likelihoodfunction can be treated as conditionally independent of the illuminant:p(x _(j) |e _(i) , S=1)=p(x _(j) |S=1)where

-   -   p(x_(j)|S=1) is a probabilistic model for self-luminous regions        Substituting, the image likelihood is computable as:        p(I|e _(i))=Π{p(x _(j) |e _(i) , S=0)p(S=0)+p(x _(j)        |S=1)p(S=1)}        From the image likelihood function, the illuminant posterior        distribution may be computed and then maximized to yield an        illuminant estimate.

In some embodiments, the posterior, p(e_(i)|I), may be computed bymultiplying the image likelihood and illuminant prior. The prior,p(e_(i)), may be used to account for the relative likelihood ofdifferent illuminants; for instance, illuminants may be expected to benear the daylight locus.

In some embodiments, a cost function may be used and computed bymultiplying the posterior by a function, which biases the result towarda reference illuminant (such as CE D6500). A bias toward a referenceilluminant can be useful because corrections associated with extremeestimated illuminants can produce undesirable resultant images if theilluminant estimate is in error.

The illuminant estimate may be the illuminant associated with themaximum after applying the cost function.

The color likelihood function given the illuminant parameters,p(x_(j)|e_(i)), is computed by making assumptions or measurements of thedistributions of colors in scenes. The purpose of this term is tocharacterize the likelihood of different colors under each illuminant.For example, under a reddish illuminant chromaticities with a nominallyreddish hue would be more likely and nominally bluish chromaticitieswould be highly unlikely.

FIG. 6 illustrates how a set of matte Munsell surfaces shifts under aset of illuminants that are near the daylight locus. Each column ofplots corresponds to a different illuminant.

FIG. 7 illustrates the chromaticities of the illuminants depicted inFIG. 6. The color of these illuminants varies in a bluish-yellowishdirection and there is a clear shift in the chromaticities of theMunsell set with each illuminant. One method of computing p(x_(j)|e_(i))is to assume that colors in images are uniformly drawn (withreplacement) from the set of Munsell matte patches. The long-termaverage color frequencies will be the same as rendering the Munsell setunder the illuminant. The probability of a given chromaticity, [x,y],under the ith illuminant will simply be the frequency at thatchromaticity of the distribution of the Munsell set under thatilluminant. An alternative method is to derive an emprical color modelfrom the surface color distributions across a large collection of imagesor objects.

In some embodiments of the present invention, a likelihood of occurrenceof specific colors may be pre-computed for a set of illuminants. Thesemay be referred to as model gamuts, p(chromaticity|e_(i)). The modelgamuts represent the probability of occurrence of each chromaticitygiven the illuminant.

The probability of being self-luminous is computed for each pixel,p(S=1|chromaticity, luminance, position). This probability is based onthe pixel's position in the image, its luminance, and its chromaticityor color characteristics. The inverse values, p(S=0|chromaticity,luminance, position), are accumulated as a function of chromaticity. Theresult of this accumulation is a histogram of p(S=0) as a function ofchromaticity, h(chromaticity).

In some embodiments, the image likelihood, p(I|e_(i)), may beapproximated as the inner product between h(chromaticity) andp(chromaticity|e_(i)). The image likelihood is computed for eachilluminant. Across all illuminants, this is a function of illuminantchromaticity, L(chromaticity).

Some embodiments of the present invention may be explained withreference to FIG. 8, which is a flowchart illustrating the steps of animage illuminant estimation process. Although the method is depicted asa sequence of numbered steps for clarity, no order should be inferredfrom the numbering unless explicitly stated. It should be understoodthat some of these steps may be skipped, performed in parallel, orperformed without the requirement of maintaining a strict order ofsequence. Initially, image element characteristics are obtained 200.These characteristics typically comprise image element colorcharacteristics, luminance characteristics and location data as well asother characteristics. Using the element characteristic data, thelikelihood that each image element is self-luminous can be determined202. This may be performed as explained in other embodiments above withreference to image element proximity to image edges or boundaries, toelement chromaticity relative to chromaticity of known illuminants andto element luminance.

A weighting factor may be assigned 204 to each element based on itslikelihood of being self-luminous. The most likely illuminant is thenestimated 206 for each element based on element characteristics. Theimage illuminant is then estimated 208 using image elementcharacteristics and the weighting factors. The image illuminant may beestimated 208 in many ways according to the methods of embodiments ofthe present invention. In some embodiments, the weighting factor may beused to adjust the contribution of elements to the image illuminantestimation process. This adjustment may comprise complete omission oflikely self-luminous elements or diminution of their contribution to theestimation process.

Other embodiments of the present invention may be explained withreference to FIG. 9, which is a flow chart showing an imagecolor-balance correction process. In these embodiments, image elementcharacteristics are obtained 220 in a typical manner as is known in theart. A likelihood that each image element is self-luminous is thendetermined 222 according to methods explained herein. A global imageilluminant is estimated 224 for the entire image based on image elementcharacteristics and the likelihood that each element is self-luminous. Acorrection factor is then computed 226 to color-balance the image forthe illuminant that has been estimated. This correction factor is thenapplied to the image 228 to achieve proper color-balance.

Other embodiments of the present invention may be explained withreference to FIG. 10, which is a flow chart illustrating a method forselective application of a color-balance correction factor. In theseembodiments, image element characteristics are obtained 240 by methodsknown in the art. A likelihood that each element is self-luminous isdetermined 242 as explained above and an image illuminant is estimated244 based on the element characteristics and the likelihood that eachelement is self-luminous. One or more correction factors are computed246 to correct the image for the illuminant and the correction factor orfactors are applied to the image 248 according to each element'slikelihood of being self-luminous. In these embodiments, the correctionfactor may be applied only to elements that are not likely to beself-luminous, may be applied in degrees according to magnitude of thelikelihood of being self-luminous or may be applied in other ways thatare proportional or otherwise related to the likelihood of an elementbeing self-luminous.

Systems and methods for determining a likelihood of an image elementbeing self-luminous, computing correction parameters and applyingdigital image correction parameters have been explained herein. A fewexamples have been given to illustrate and clarify aspects of theinvention. However, the invention is not limited to merely theseexamples. Specific correction algorithms have also been given to providea context for the invention, but again, the invention is not limited tojust the mentioned algorithms. Other variations and embodiments of theinvention will occur to those skilled in the art.

1. A method for determining that an image element is likely to beself-luminous, the method comprising: a. determining image elementcharacteristics; b. comparing the characteristics of said image elementto those for known self-luminous elements wherein said comparingcomprises at least one act taken from the set consisting of: (i)comparing the proximity of said image element to image boundaries withthe proximity of known image elements to their boundaries, (ii)comparing the color characteristics of said image element tocharacteristics of a known illuminant, and (iii)comparing the luminancecharacteristics of said image element to characteristics of knownself-luminous elements; c. assigning a self-luminosity weight factor tosaid image element; and d. estimating a color balance correction for atleast a portion of said image wherein said correction is based on saidweight factor
 2. A method for determining that an image element islikely to be self-luminous, the method comprising: a. determining imageelement characteristics; b. comparing the color characteristics of saidimage element to those found under a known illuminant; c. comparing theluminance characteristics of said image element to those found under aknown illuminant; and d. classifying said image element as likely to beself-luminous when at least one of said color characteristics and saidluminance characteristics meet a criteria for self-luminous elements. 3.A method as described in claim 2 further comprising measuring theproximity of said image element to an image boundary and wherein saidclassifying further comprises evaluation of said proximity to determinewhether said criteria are met.
 4. A method for estimating the illuminantof an image, the method comprising: a. determining image elementcharacteristics; b. assigning a weighting factor to each image elementaccording to its likelihood of being self-luminous; c. estimating anilluminant for a plurality of image elements; d. estimating an imageilluminant based on said illuminants for each image element adjusted bysaid weighting factors.
 5. A method as described in claim 4 wherein theeffect of said weighting factor is proportional to the likelihood thatan image element is non-self-luminous.
 6. A method of correctingcolor-balance in an image, the method comprising: a. obtaining imageelement characteristics for an image; b. assigning a weighting factor toeach image element according to its likelihood of being self-luminous;c. estimating an image illuminant based on said image elementcharacteristics and said weighting factors; and d. correcting imagecolor-balance for said estimated illuminants.
 7. A method as describedin claim 6 wherein said correcting comprises: a. correcting imageelements that are not likely to be self-luminous for the estimatedilluminant; and b. omitting said correcting image color-balance forimage elements that are likely to be self-luminous.
 8. A method asdescribed in claim 6 wherein said correcting comprises: a. correctingsaid image elements according to their likelihood of being self-luminouswherein a full correction is applied to elements that are least likelyto be self-luminous, no correction is applied to elements that are mostlikely to be self-luminous and a partial correction is applied toelements that fall between these limits.
 9. A set of executableinstructions for determining that an image element is likely to beself-luminous, the method comprising: a. determining image elementcharacteristics; b. comparing the characteristics of said image elementto those for known self-luminous elements wherein said comparingcomprises at least one act taken from the set consisting of: (i)comparing the proximity of said image element to image boundaries withthe proximity of known image elements to their boundaries, (ii)comparing the color characteristics of said image element to those ofknown illuminant, and (iii)comparing the luminance characteristics ofsaid image element to those of known self-luminous elements, and c.classifying said image element as likely to be self-luminous when atleast one of said proximity, said color characteristics and saidluminance characteristics meet a criteria for self-luminous elements.10. A system for determining that an image element is likely to beself-luminous, the system comprising: a. a storage for storing imageelement characteristics; b. a processor for comparing thecharacteristics of said image element to those for known self-luminouselements wherein said comparing comprises at least one act taken fromthe set consisting of: i. comparing the proximity of said image elementto image boundaries with the proximity of known image elements to theirboundaries, ii. comparing the color characteristics of said imageelement to those of known illuminants, and iii. comparing the luminancecharacteristics of said image element to those of known self-luminouselements, and c. a classifier for classifying said image element aslikely to be self-luminous when at least one of said proximity, saidcolor characteristics and said luminance characteristics meet a criteriafor self-luminous elements.